JP5362845B2 - Low complexity channel estimation for uplink receivers - Google Patents

Abstract

The present invention proposes an LTE eNodeB receiver channel estimation technique that is referred to as reduced complexity minimum mean squared error (MMSE) technique for channel estimation. From the invention's assumptions, estimations and modified calculations, the present invention generates precise channel estimates of RS using the reduced complexity MMSE matrix and previously computed LS channel estimates HLS is as follows: (Formula I) which generates precise channel estimates of RS using the reduced complexity MMSE matrix and previously computed LS channel estimates. As a second aspect of the present invention, it is desired that the SNR be estimated within -3 dB of the actual channel SNR. As a third aspect of the invention, an adaptive method of data channel interpolation from RS channel is being proposed in this invention.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application is related to provisional patent application 61 / 114,346 filed November 13, 2008, provisional patent application 61 / 122,553 filed December 15, 2008, and 2009. In relation to provisional patent application 61 / 151,644 filed on February 11, S. C. The priority of this earlier application is claimed under §119 (e). This provisional patent application is also incorporated by reference into this patent application.

  This application relates generally to wireless communication technologies, and more particularly to improving user equipment connectivity by increasing the accuracy of channel estimation through the use of low complexity channel estimation and reducing overhead on the system.

  An IP-based mobile system includes at least one mobile node on a wireless communication system. “Mobile node” may also be referred to as a user equipment, mobile unit, mobile terminal, mobile device, or the like, depending on the terminology employed by a particular system provider. Various components on the system may be referred to by different names depending on the terminology used on any particular network configuration or communication system.

  For example, a “mobile node” or “user equipment” is connected to a wireless network by cable (eg, telephone line (“twisted wire”), Ethernet cable, optical cable, etc.), as well as Internet access, email, Includes PCs wirelessly connected directly to cellular networks so that they can be experienced by different brands and models of mobile terminals (“cell phones”) with different features and functions such as messaging services To do. The term “mobile node” also includes mobile communication units (eg, mobile terminals, “smart phones”, nomadic devices such as wirelessly connected laptop PCs).

  A user equipment or mobile node is a receiver of signals in the uplink direction of signals transmitted from an access point, called a transmitter in its configuration. Terms such as transmitter or receiver are not limitingly defined and may include various mobile communication units or transmitting devices located on the network. Further, the terms “receiver” and “transmitter” may be referred to as an “access point (AP)”, “base station”, or “user equipment” depending on which direction the communication is transmitted and received. For example, in the downlink environment, the access point AP or base station (eNodeB or eNB) is a transmitter and the user equipment is a receiver, whereas in the uplink environment, the access point AP or base station (eNodeB). Or eNB) is a receiver and the user equipment is a transmitter.

  Accurate channel estimation maintains good connectivity and good capacity and throughput on uplink communication links that support transmissions from mobile nodes (or user equipment) on LTE radio systems to base stations (or access points) It is essential to achieve performance. When the mobile node or user equipment is moving fast relative to the access point (transmitter), the known channel estimation method has two main effects that can negatively impact system performance. It has various defects. First, known channel estimation methods (eg, Minimum Mean Square Error (MMSE) based estimation) are found to be inaccurate because user equipment (or mobile nodes) move at high speed. Secondly, the known channel estimation formula is computationally intensive, which is required to unnecessarily consume system resources, increase system overhead and complete channel estimation. Increase the amount of time Therefore, in order to achieve good capacity / throughput performance at high mobility and / or high channel frequency, performance degradation issues due to channel estimation inaccuracy need to be addressed, There is a need to reduce the complexity of the estimation algorithm.

ここで、
・ Ｒ_ＨＨ＝Ｅ［ＨＨ^＊］は周波数領域チャネル相関行列であり、Ｈは周波数領域チャネル応答であり、^＊は共役転置を示し、
・ Ｘは、既知のパイロットまたは既知の基準シンボル（ＲＳ）シーケンスを含むベクトルであり、
・

は、チャネル雑音の分散であり、
・

は、チャネルの最小二乗（ＬＳ）推定値であり、ここで、ｙは受信されたＲＳシンボルのベクトルである。 A minimum mean square error (MMSE) based channel estimator utilizes channel state second order statistics to minimize the mean square error of the channel estimate. The basic assumption is that the time domain channel vector is Gaussian and has no correlation with channel noise. The linear MMSE channel estimate is given as:

here,
R _HH = E [HH ^* ] is the frequency domain channel correlation matrix, H is the frequency domain channel response, ^* indicates the conjugate transpose,
X is a vector containing a known pilot or known reference symbol (RS) sequence;
・

Is the variance of the channel noise,
・

Is the least squares (LS) estimate of the channel, where y is the vector of received RS symbols.

  The MMSE estimator provides much better performance than the LS channel estimator alone, especially under low SNR scenarios such as slow user equipment. However, a major drawback of the MMSE estimator is its high computational complexity due to increased consumption of system resources and increased system overhead from executing the entire MMSE equation.

  In Equation (1), X represents a known reference signal (RS) sequence transmitted by the UE. Note that in the LTE standard, sequence hopping and group hopping can accommodate uplink RS sequences. When sequence / group hopping is enabled, the above MMSE expression requires that two matrix transposes be performed every slot (0.5 milliseconds). The matrix size is 12 × 12 for one RB allocation for any user, while the matrix size is 576 × 576 for all 48 RB allocations for one user. Such real-time matrix inversion is computationally intensive for actual execution, and this MMSE expression may be inaccurate because the user equipment (or mobile node) moves at a higher speed. I understand.

  It should also be noted that signal-to-noise ratio (SNR) estimation is an essential processing step in eNodeB receivers in some situations, and system performance is degraded if there is an underestimation of SNR. Therefore, to increase system performance and minimize system parameter degradation, there is a need to accurately estimate the SNR for use in some channel estimation procedures.

  It should further be noted that channel estimation methods also use interpolation, and some techniques for interpolation (eg linear, bilinear, or quadratic, etc.) do not provide sufficient results in some situations. . These approaches to interpolation are static and in general the same approach is used for all UE mobility and therefore does not provide optimal SNR vs. SER performance in some situations.

  The present invention proposes an LTE eNodeB receiver channel estimation technique called low complexity minimum mean square error (MMSE) technique for channel estimation. This technique provides channel estimation / demodulation performance comparable to that of MMSE channel estimation at a greatly reduced implementation complexity (hardware / computational complexity).

を推定し、ここでｙは、ＲＳシンボルの受信されたベクトルであり、ＸはＵＬ ＲＳシンボルにおける既知の送信されたＣＡＺＡＣシーケンスのベクトルであり、（１３）ユーザ機器が連続して送信している場合は、前のスロットまたはサブフレームからＳＮＲ推定値を得るか、または断続的送信の場合は、チャネル推定値

を使用してＳＮＲを推定し、（１４）βの既知の値（ＱＰＳＫのためには１、１６−ＱＡＭのためには０．５２９４、６４−ＱＡＭのためには０．３７２４）、ＳＮＲ推定値、およびＲ_ＨＨを使用する。 The low complexity MMSE equation [reduced complexity MMSE equation] is obtained by making several key assumptions based on known system performance attributes and system performance parameters as follows: (1) Replace the term (XX ^* ) ⁻¹ with its expected value E {(XX ^* ) ⁻¹ }, and (2) the signal constellation is the same on all subcarriers in the RS symbol and all constellations E {(XX ^* ) ⁻¹ } = E {| 1 / Xk | ² } ^* I, where I is the identity matrix, and (3) average SNR = E {| Xk | ² } / σ _N ² , term β = E {| Xk | ² } / E {| 1 / Xk | ² }, and term σ _N ² (XX ^* ) ⁻¹ = (β / SNR) Define β, where β is a constant that depends only on the signal constellation, (4) β = 1 for QPSK constellation, β = 0.5294, 64-−for 16QAM constellation Β = 0.3724 for QAM transmission, (5) The TE uplink _{transmission, R HH} matrix is sub-carrier spacing and channel r. m. depends on s delay spread only, and (6) the same matrix is used for several subframes until another SNR estimate is received (even if sequence hopping and group hopping are enabled) (7) takes advantage of the simplicity in generating channel autocorrelation for LTE uplink RS patterns and (8) is a widely accepted industry standard for channel power profiles ) Assuming exponential decay power delay profile, (9) Assuming Jakes spectrum for Doppler spread, (10) In LTE uplink RS pattern, the time correlation function between RS patterns is 1, which is Due to the fact that all reference symbols are in the same OFDM symbol, (11) when generating R _HH R. m. Assume a value of 2 microseconds for the s delay spread. This is because the accuracy of the channel estimate is r. m. Based on simulation results showing that it is not very sensitive to s delay spread values, every (12) slots (0.5 ms duration)

Where y is a received vector of RS symbols, X is a vector of known transmitted CAZAC sequences in UL RS symbols, and (13) user equipment is continuously transmitting If the SNR estimate is obtained from the previous slot or subframe, or in the case of intermittent transmission, the channel estimate

(14) Known values of β (1 for QPSK, 0.5294 for 16-QAM, 0.3724 for 64-QAM), SNR estimation Values and R _HH are used.

を使用してＲＳの正確なチャネル推定値を生成する。

ＲＳのＭＭＳＥ推定値から、データ・チャネル推定値が線形補間を使用して生成されることが可能である。本発明は、すべてのタイプのフェーディング・シナリオの下でのＬＴＥ ＳＣ−ＦＤＭＡアップリンクにおける不正確なチャネル推定に起因するＳＮＲ低下の問題に対処する。さらに、本発明は、ＬＴＥアップリンク受信機に関連するチャネル推定問題に対する解決策を提供する。このチャネル推定方法によって、すべてのタイプのフェーディング・チャネル・シナリオの下で良好な復調性能が維持される。さらに、この方法の複雑性はＭＭＳＥ技術よりかなり低く、したがってＬＴＥソフトウェア・リリースのために基地局（ｅＮｏｄｅＢ）のハードウェアにおいて容易に実施されることが可能である。 From these assumptions, estimates, and modified calculations, the present invention provides a low complexity MMSE matrix and pre-calculated LS channel estimates as follows:

Is used to generate an accurate channel estimate of the RS.

From the RS MMSE estimate, a data channel estimate can be generated using linear interpolation. The present invention addresses the problem of SNR degradation due to inaccurate channel estimation in the LTE SC-FDMA uplink under all types of fading scenarios. Furthermore, the present invention provides a solution to the channel estimation problem associated with LTE uplink receivers. This channel estimation method maintains good demodulation performance under all types of fading channel scenarios. Furthermore, the complexity of this method is much lower than that of MMSE technology and can therefore be easily implemented in base station (eNodeB) hardware for LTE software releases.

の低複雑性最小平均二乗誤差（ＭＭＳＥ）式を使用して計算され、
前記低複雑性最小平均二乗誤差式は、システム性能に関して複数の仮定を行い、式計算
の複雑性を低減するために前記最小平均二乗誤差式における因数を置き換え、その結果、
処理時間が低減され、システム・オーバヘッド使用量が低減されることになる、
方法。
（項目２）
１つの置き換えられた因数が、前記最小平均二乗誤差式における項（ＸＸ ^＊ ） ^−１ をそ
の期待値Ｅ｛（ＸＸ ^＊ ） ^−１ ｝で置き換える、項目１に記載の方法。
（項目３）
システム性能に関する１つの仮定が、信号コンステレーションがＲＳシンボルにおける
すべての副搬送波上で同じであり、すべてのコンステレーション・ポイント上で等しい確
率を有し、Ｅ｛（ＸＸ ^＊ ） ^−１ ｝＝Ｅ｛｜１／Ｘｋ｜ ^２ ｝Ｉであることであり、ここでＩ
は単位行列である、項目１に記載の方法。
（項目４）
システム性能に関する１つの仮定が、平均ＳＮＲ＝Ｅ｛｜Ｘｋ｜ ^２ ｝／σ _Ｎ ^２ 、および
項β＝Ｅ｛｜Ｘｋ｜ ^２ ｝／Ｅ｛｜１／Ｘｋ｜ ^２ ｝、項σ _Ｎ ^２ （ＸＸ ^＊ ） ^−１ ＝（β／ＳＮ
Ｒ）Ｉを定義することであり、ここでβは、信号コンステレーションのみに依存する定数
である、項目１に記載の方法。
（項目５）
システム性能に関する１つの仮定が、下記、すなわちＱＰＳＫコンステレーションのた
めには、β＝１、１６ＱＡＭコンステレーションのためには、β＝０．５２９４、６４−
ＱＡＭ送信のためには、β＝０．３７２４、のうちの１つを定義することである、項目
１に記載の方法。
（項目６）
システム性能に関する１つの仮定が、Ｒ _ＨＨ 行列が副搬送波間隔、およびＬＴＥアップ
リンク送信のためのチャネルのｒ．ｍ．ｓ遅延スプレッドのみに依存すると仮定すること
である、項目１に記載の方法。
（項目７）
システム性能に関する１つの仮定が、たとえシーケンス・ホッピングおよびグループ・
ホッピングがイネーブルにされても、別のＳＮＲ推定値が受信されるまで、同じ行列がい
くつかのサブフレームのために使用されることが可能であると仮定することである、請求
項１に記載の方法。
（項目８）
前記式における１つの低減された因数が、ＬＴＥアップリンクＲＳパターンのチャネル
自己相関の生成時の簡単さを利用することにより得られる、項目１に記載の方法。
（項目９）
システム性能に関する１つの仮定が、（チャネル電力プロファイルのための広く受け入
れられている業界標準である）指数減衰電力遅延プロファイルを仮定することである、請
求項１に記載の方法。
（項目１０）
１つの置き換えられた因数が前記ＬＴＥアップリンクＲＳパターンのために前記ＲＳパ
ターン間の時間相関関数を１にセットし、このことはすべての基準シンボルが同じＯＦＤ
Ｍシンボルにあるという事実と相関関係がある、項目１に記載の方法。
（項目１１）
システム性能に関する１つの仮定が、チャネル推定値の正確さがｒ．ｍ．ｓ遅延スプレ
ッド値にあまり敏感でないことをシミュレーションが示すので、Ｒ _ＨＨ の生成時の前記ｒ
．ｍ．ｓ遅延スプレッドのために２マイクロ秒の値を仮定することである、項目１に記
載の方法。
（項目１２）
システム性能に関する１つの仮定が、スロット（０．５ミリ秒の継続時間）ごとに、

を推定することであり、ｙはＲＳシンボルの受信されたベクトルであり、ＸはＵＬ ＲＳ
シンボルにおける既知の送信されたＣＡＺＡＣシーケンスのベクトルである、項目１に
記載の方法。
（項目１３）
システム性能に関する１つの仮定が、ユーザ機器が連続して送信している場合は、前の
スロット／サブフレームからＳＮＲ推定値を得ることである、項目１に記載の方法。
（項目１４）
システム性能に関する１つの仮定が、チャネル推定性能がＳＮＲ推定誤差に対して強靭
であることをシミュレーションが示すので、断続的送信の場合は、チャネル推定値

を使用してＳＮＲ推定値を得ることである、項目１に記載の方法。
（項目１５）
システム性能に関する１つの仮定が、βの既知の値（ＱＰＳＫのためには１、１６−Ｑ
ＡＭのためには０．５２９４、６４−ＱＡＭのためには０．３７２４）、ＳＮＲ推定値、
およびＲ _ＨＨ を使用することである、項目１に記載の方法。
（項目１６）
線形補間、および基準信号の最小平均二乗誤差推定値を使用してデータ・チャネル推定
値を生成するステップをさらに含む、項目１に記載の方法。
（項目１７）
ＳＮＲが実際のチャネルＳＮＲの−３ｄＢ以内で推定される、項目１に記載の方法。
（項目１８）
適応ステップ、最大ドップラー・スプレッドの推定値、および基準信号チャネルを使用
してデータ・チャネルを補間するステップをさらに含む、項目１に記載の方法。
（項目１９）
無線通信システムにおいてアップリンク受信機チャネルを推定する送信システムであっ
て、
前記無線システム上の送信機およびアップリンク受信機を備え、前記アップリンク受信
機チャネル推定値が、前記アップリンク受信機のための前記チャネルを推定するために、

の低複雑性最小平均二乗誤差（ＭＭＳＥ）式を使用して計算され、
前記低複雑性最小平均二乗誤差式が、システム性能に関する複数の仮定を行い、式計算
の複雑性を低減するために前記最小平均二乗誤差式における因数を置き換え、その結果、
処理時間が低減され、システム・オーバヘッド使用量が低減されることになる、
システム。
（項目２０）
１つの置き換えられた因数が、前記最小平均二乗誤差式における項（ＸＸ ^＊ ） ^−１ をそ
の期待値Ｅ｛（ＸＸ ^＊ ） ^−１ ｝で置き換える、項目１９に記載のシステム。
（項目２１）
システム性能に関する１つの仮定が、信号コンステレーションがＲＳシンボルにおける
すべての副搬送波上で同じであり、すべてのコンステレーション・ポイント上で等しい確
率を有し、Ｅ｛（ＸＸ ^＊ ） ^−１ ｝＝Ｅ｛｜１／Ｘｋ｜ ^２ ｝Ｉであることであり、ここでＩ
は単位行列である、項目１９に記載のシステム。
（項目２２）
システム性能に関する１つの仮定が、平均ＳＮＲ＝Ｅ｛｜Ｘｋ｜ ^２ ｝／σ _Ｎ ^２ および項
β＝Ｅ｛｜Ｘｋ｜ ^２ ｝／Ｅ｛｜Ｘｋ｜ ^２ ｝、項σ _Ｎ ^２ （ＸＸ ^＊ ） ^−１ ＝（β／ＳＮＲ）Ｉ
を定義することであり、ここでβは、信号コンステレーションのみに依存する定数である
、項目１９に記載のシステム。
（項目２３）
システム性能に関する１つの仮定が、下記、すなわちＱＰＳＫコンステレーションのた
めにはβ＝１、１６ＱＡＭコンステレーションのためにはβ＝０．５２９４、および６４
−ＱＡＭ送信のためにはβ＝０．３７２４のうちの１つを定義することである、項目１
９に記載のシステム。
（項目２４）
システム性能に関する１つの仮定が、Ｒ _ＨＨ 行列が副搬送波間隔、およびＬＴＥアップ
リンク送信のためのチャネルのｒ．ｍ．ｓ遅延スプレッドのみに依存すると仮定すること
である、項目１９に記載のシステム。
（項目２５）
システム性能に関する１つの仮定が、たとえシーケンス・ホッピングおよびグループ・
ホッピングがイネーブルにされても、別のＳＮＲ推定値が受信されるまで、同じ行列がい
くつかのサブフレームのために使用されることが可能であると仮定することである、請求
項１９に記載のシステム。
（項目２６）
前記式における１つの低減された因数が、ＬＴＥアップリンクＲＳパターンのチャネル
自己相関の生成時の簡単さを利用することにより得られる、項目１９に記載のシステム
。
（項目２７）
システム性能に関する１つの仮定が、（チャネル電力プロファイルのための広く受け入
れられている業界標準である）指数減衰電力遅延プロファイルを仮定することである、請
求項１９に記載のシステム。
（項目２８）
１つの置き換えられた因数が前記ＬＴＥアップリンクＲＳパターンのために前記ＲＳパ
ターン間の時間相関関数を１にセットし、このことはすべての基準シンボルが同じＯＦＤ
Ｍシンボルにあるという事実と相関関係がある、項目１９に記載のシステム。
（項目２９）
システム性能に関する１つの仮定が、チャネル推定値の正確さがｒ．ｍ．ｓ遅延スプレ
ッド値にあまり敏感でないことをシミュレーションが示すので、Ｒ _ＨＨ の生成時の前記ｒ
．ｍ．ｓ遅延スプレッドのために２マイクロ秒の値を仮定することである、項目１９に
記載のシステム。
（項目３０）
システム性能に関する１つの仮定が、スロット（０．５ミリ秒の継続時間）ごとに、

を推定することであり、ｙはＲＳシンボルの受信されたベクトルであり、ＸはＵＬ ＲＳ
シンボルにおける既知の送信されたＣＡＺＡＣシーケンスのベクトルである、項目１９
に記載のシステム。
（項目３１）
システム性能に関する１つの仮定が、ユーザ機器が連続して送信している場合は、前の
スロット／サブフレームからＳＮＲ推定値を得ることである、項目１９に記載のシステ
ム。
（項目３２）
システム性能に関する1つの仮定が、チャネル推定性能がＳＮＲ推定誤差に対して強靭
であることをシミュレーションが示すので、断続的送信の場合は、チャネル推定値

を使用してＳＮＲ推定値を得ることである、項目１９に記載のシステム。
（項目３３）
システム性能に関する１つの仮定が、βの既知の値（ＱＰＳＫのためには１、１６−Ｑ
ＡＭのためには０．５２９４、６４−ＱＡＭのためには０．３７２４）、ＳＮＲ推定値、
およびＲ _ＨＨ を使用することである、項目１９に記載のシステム。
（項目３４）
前記送信機が、線形補間、および基準信号の最小平均二乗誤差推定値を使用してデータ
・チャネル推定値を生成する、項目１９に記載のシステム。
（項目３５）
ＳＮＲが実際のチャネルＳＮＲの−３ｄＢ以内で推定される、項目１９に記載のシス
テム。
（項目３６）
前記受信機が、適応ステップ、最大ドップラー・スプレッドの推定値、および基準信号
チャネルを使用してデータ・チャネルを補間する、項目１９に記載のシステム。
（項目３７）
アップリンク受信機チャネルを推定する無線通信システムであって、送信機および受信
機を有し、前記アップリンク受信機チャネルが、前記アップリンク受信機のためのチャネ
ルを推定するために、

の低複雑性最小平均二乗誤差（ＭＭＳＥ）式を使用して計算される、システム。
（項目３８）
前記受信機が、線形補間、および基準信号の最小平均二乗誤差推定値を使用してデータ
・チャネル推定値を生成する、項目３７に記載のシステム。
（項目３９）
ＳＮＲが実際のチャネルＳＮＲの−３ｄＢ以内で推定される、項目３７に記載のシス
テム方法。
（項目４０）
前記受信機が、適応ステップ、最大ドップラー・スプレッドの推定値、および基準信号
チャネルを使用してデータ・チャネルを補間する、項目３７に記載のシステム。
As a second aspect of the invention, signal-to-noise ratio (SNR) estimation is an essential processing step in an eNodeB receiver for use with low complexity least mean square error (RC-MMSE) channel estimation. Please keep in mind. The present invention (low complexity MMSE channel estimation)
The performance of this technique has some sensitivity to the estimated SNR. Demodulated SER versus actual SNR has been measured for SNR estimation error in the range of +/- 3 dB. Note that overestimation of the SNR will yield slightly better performance, while underestimation of the SNR will yield worse performance. In order to limit performance degradation, it is desirable that the SNR be estimated within -3 dB of the actual channel SNR. As a third aspect of the present invention, an adaptive method of data channel interpolation based on RS signals is proposed in the present invention.
For example, the present invention provides the following items.
(Item 1)
An uplink receiver channel in a wireless communication system having a transmitter and a receiver
A method for estimating,
Providing a transmitter and an uplink receiver on the wireless system;
The uplink receiver channel estimates the channel for the uplink receiver
To do

Calculated using the low complexity minimum mean square error (MMSE) formula of
The low complexity minimum mean square error equation makes multiple assumptions regarding system performance and calculates the formula
Replacing the factor in the least mean square error equation to reduce the complexity of
Processing time will be reduced and system overhead usage will be reduced,
Method.
(Item 2)
One replaced factor is, terms in the minimum mean square error formula (XX ^{*) -1} to its
The method according to Item 1, wherein the expected value E {(XX ^* ) ⁻¹ } is replaced with.
(Item 3)
One assumption regarding system performance is that the signal constellation is
Same on all subcarriers and equal on all constellation points.
Where E {(XX ^* ) ⁻¹ } = E {| 1 / Xk | ² } I, where I
The method according to item 1, wherein is a unit matrix.
(Item 4)
One assumption regarding system performance is that the average SNR = E {| Xk | ² } / σ _N ² , and
The term β = E {| Xk | ² } / E {| 1 / Xk | ² }, the term σ _N ² (XX ^* ) ⁻¹ = (β / SN
R) defining I, where β is a constant that depends only on the signal constellation
The method according to item 1, wherein
(Item 5)
One assumption regarding system performance is the following: QPSK constellation
For β = 1, for 16QAM constellations, β = 0.5294, 64-
For QAM transmission, the item is to define one of β = 0.3724
The method according to 1.
(Item 6)
One assumption regarding system performance is that the R _HH matrix is subcarrier spacing and LTE up
R. Of channel for link transmission. m. Assuming that it depends only on s delay spread
The method according to item 1, wherein
(Item 7)
One assumption about system performance is that even sequence hopping and group
Even if hopping is enabled, the same matrix remains until another SNR estimate is received.
Claiming that it can be used for some subframes
Item 2. The method according to Item 1.
(Item 8)
One reduced factor in the equation is the channel of the LTE uplink RS pattern
Item 2. The method according to item 1, obtained by using the simplicity in generating the autocorrelation.
(Item 9)
One assumption regarding system performance is that it is widely accepted (for channel power profiles
Is assumed to be an exponentially damped power delay profile
The method according to claim 1.
(Item 10)
One replaced factor is the RS parameter for the LTE uplink RS pattern.
The time correlation function between turns is set to 1, which means that all reference symbols have the same OFD
Item 2. The method of item 1, wherein the method correlates with the fact that it is in the M symbol.
(Item 11)
One assumption regarding system performance is that the accuracy of the channel estimate is r. m. s delay spray
Since the show is simulation that the head value not very sensitive, the r at the time of generation of R _HH
. m. As described in item 1, which assumes a value of 2 microseconds for the s delay spread.
The method of publication.
(Item 12)
One assumption regarding system performance is that every slot (0.5 ms duration)

, Y is the received vector of RS symbols, and X is UL RS
Item 1, which is a vector of known transmitted CAZAC sequences in symbols
The method described.
(Item 13)
One assumption regarding system performance is that if the user equipment is transmitting continuously, the previous
Item 2. The method of item 1, wherein the method is to obtain an SNR estimate from a slot / subframe.
(Item 14)
One assumption about system performance is that channel estimation performance is robust to SNR estimation errors
The simulation shows that, for intermittent transmissions, the channel estimate

2. The method of item 1, wherein the method is to obtain an SNR estimate using.
(Item 15)
One assumption about system performance is that the known value of β (1, 16-Q for QPSK
0.5294 for AM, 0.3724 for 64-QAM), SNR estimate,
2. The method of item 1, wherein the method is using _RHH .
(Item 16)
Data channel estimation using linear interpolation and minimum mean square error estimate of reference signal
The method of item 1, further comprising the step of generating a value.
(Item 17)
Item 2. The method of item 1, wherein the SNR is estimated within -3 dB of the actual channel SNR.
(Item 18)
Use adaptation step, maximum Doppler spread estimate, and reference signal channel
The method of item 1, further comprising: interpolating the data channel.
(Item 19)
A transmission system for estimating an uplink receiver channel in a wireless communication system.
And
An uplink receiver comprising a transmitter and an uplink receiver on the wireless system;
A machine channel estimate to estimate the channel for the uplink receiver,

Calculated using the low complexity minimum mean square error (MMSE) formula of
The low complexity least mean square error equation makes multiple assumptions regarding system performance and calculates the formula
Replacing the factor in the least mean square error equation to reduce the complexity of
Processing time will be reduced and system overhead usage will be reduced,
system.
(Item 20)
One replaced factor is, terms in the minimum mean square error formula (XX ^{*) -1} to its
Item 20. The system according to Item 19, wherein the system is replaced with an expected value E {(XX ^* ) ^-1 }.
(Item 21)
One assumption regarding system performance is that the signal constellation is
Same on all subcarriers and equal on all constellation points.
Where E {(XX ^* ) ⁻¹ } = E {| 1 / Xk | ² } I, where I
The system according to item 19, wherein is a unit matrix.
(Item 22)
One assumption regarding system performance is that the average SNR = E {| Xk | ² } / σ _N ² and the term
β = E {| Xk | ² } / E {| Xk | ² }, term σ _N ² (XX ^* ) ⁻¹ = (β / SNR) I
Where β is a constant that depends only on the signal constellation
The system according to item 19.
(Item 23)
One assumption regarding system performance is the following: QPSK constellation
Β = 1, for 16QAM constellation β = 0.5294, and 64
Item 1 is to define one of β = 0.3724 for QAM transmission
10. The system according to 9.
(Item 24)
One assumption regarding system performance is that the R _HH matrix is subcarrier spacing and LTE up
R. Of channel for link transmission. m. Assuming that it depends only on s delay spread
20. The system according to item 19, wherein
(Item 25)
One assumption about system performance is that even sequence hopping and group
Even if hopping is enabled, the same matrix remains until another SNR estimate is received.
Claiming that it can be used for some subframes
Item 20. The system according to Item 19.
(Item 26)
One reduced factor in the equation is the channel of the LTE uplink RS pattern
Item 20. The system according to item 19, obtained by using simplicity when generating autocorrelation.
.
(Item 27)
One assumption regarding system performance is that it is widely accepted (for channel power profiles
Is assumed to be an exponentially damped power delay profile
The system according to claim 19.
(Item 28)
One replaced factor is the RS parameter for the LTE uplink RS pattern.
The time correlation function between turns is set to 1, which means that all reference symbols have the same OFD
Item 20. The system of item 19, which correlates with the fact that it is in the M symbol.
(Item 29)
One assumption regarding system performance is that the accuracy of the channel estimate is r. m. s delay spray
Since the show is simulation that the head value not very sensitive, the r at the time of generation of R _HH
. m. s to assume a value of 2 microseconds for the delay spread, item 19
The described system.
(Item 30)
One assumption regarding system performance is that every slot (0.5 ms duration)

, Y is the received vector of RS symbols, and X is UL RS
Item 19 which is a vector of known transmitted CAZAC sequences in symbols
The system described in.
(Item 31)
One assumption regarding system performance is that if the user equipment is transmitting continuously, the previous
The system according to item 19, wherein the SNR estimate is obtained from a slot / subframe.
Mu.
(Item 32)
One assumption about system performance is that channel estimation performance is robust to SNR estimation errors
The simulation shows that, for intermittent transmissions, the channel estimate

20. The system of item 19, wherein the system is to obtain an SNR estimate using.
(Item 33)
One assumption about system performance is that the known value of β (1, 16-Q for QPSK
0.5294 for AM, 0.3724 for 64-QAM), SNR estimate,
And it is the use of _{R HH,} system of claim 19.
(Item 34)
The transmitter uses linear interpolation and a minimum mean square error estimate of the reference signal for data
A system according to item 19, generating a channel estimate.
(Item 35)
Item 20. The cis of item 19, wherein the SNR is estimated within -3 dB of the actual channel SNR.
System.
(Item 36)
The receiver includes an adaptation step, an estimate of maximum Doppler spread, and a reference signal
20. The system of item 19, wherein the channel is used to interpolate the data channel.
(Item 37)
A wireless communication system for estimating an uplink receiver channel comprising a transmitter and a receiver
And the uplink receiver channel is a channel for the uplink receiver.
To estimate

The system is calculated using a low complexity minimum mean square error (MMSE) equation.
(Item 38)
The receiver uses linear interpolation and a minimum mean square error estimate of the reference signal for data
40. A system according to item 37, wherein the system generates a channel estimate.
(Item 39)
40. The system of item 37, wherein the SNR is estimated within −3 dB of the actual channel SNR.
System method.
(Item 40)
The receiver includes an adaptation step, an estimate of maximum Doppler spread, and a reference signal
40. The system of item 37, wherein the channel is used to interpolate the data channel.

  Embodiments of the present invention will now be described by way of example only with reference to the accompanying drawing figures.

1 is a block diagram of a communication system using the present invention. It is a figure which shows the block allocated to the cellular spectrum in LTE transmission.

  Exemplary components of a system using the present invention include a base station 1000 and a mobile station 1002, as shown in FIG. Base station 1000 includes a wireless interface 1004 for communicating wirelessly with a wireless interface 1006 in mobile station 1002 via a wireless link. Base station 1000 includes software 1008 that is executable on one or more central processing units (CPUs) 1010 in base station 1000 to perform base station tasks. The CPU (s) 1010 is connected to the memory 1012. Software 1008 may include a scheduler and other software modules. Base station 1000 also includes an inter-base station interface 1014 for exchanging information such as backhaul information and / or coordination information with another base station.

  Similarly, mobile station 1002 includes software 1016 that is executable on one or more CPUs 1018 connected to memory 1020. Software 1016 can be executed to perform the tasks of mobile station 1002. Such software (1008 and 1016) instructions may be loaded for execution on a CPU or other type of processor. A processor can be a microprocessor, microcontroller, processor module or subsystem (including one or more microprocessors or microcontrollers), or other control or computing device. A “processor” can refer to a single component or multiple components.

  Data and (software) instructions are stored in respective storage devices implemented as one or more computer-readable or computer-usable storage media. Storage medium includes dynamic random access memory or static random access memory (DRAM or SRAM), erasable programmable read only memory (EPRROM), electrically erasable programmable read only memory (ERPROM) and flash memory Semiconductor memory devices such as, magnetic disks such as fixed disks, floppy disks and removable disks, other magnetic media including tapes, and optical media such as compact disks (CD) or digital video disks (DVD) Including various forms of memory.

  The channel is estimated based on indicator signals that are periodically broadcast on the forward or uplink in the frame signaling structure. In this embodiment, the mobile station 1002 will receive indicator channel signaling received from the transmission. Mobile station 1002 may be referred to as an access terminal. The mobile station 1002 can perform channel estimation calculations or provide feedback to the base station 1000 based on the channel estimation protocol being performed, in which case the base station performs the channel estimation calculations. Become.

  From the channel estimation, base station 1000 or mobile station 1002 determines how mobile station 1002 is served, and base station 1000 determines whether mobile station 1002 needs to monitor the communication channel. can do. Base station 1000 can also schedule communications to be transmitted from base station 1002 based on channel estimation calculations. Base station 1000 can also schedule users to avoid interference and overlapping beam conditions.

ここで、
・ Ｒ_ＨＨ＝Ｅ［ＨＨ^＊］は、周波数領域チャネル相関行列であり、Ｈは周波数領域チャネル応答であり、^＊は共役転置を表し、
・ Ｘは、既知のパイロットまたは既知の基準シンボル（ＲＳ）シーケンスを含むベクトルであり、
・

は、チャネル雑音の分散であり、
・

は、チャネルの最小二乗（ＬＳ）推定値であり、ここでｙは受信されたＲＳシンボルのベクトルである。 A minimum mean square error (MMSE) based channel estimator utilizes channel state second order statistics to minimize the mean square error of the channel estimate. The basic assumption is that the time domain channel vector is Gaussian and has no correlation with channel noise. The linear MMSE channel estimate is given as:

here,
R _HH = E [HH ^* ] is the frequency domain channel correlation matrix, H is the frequency domain channel response, ^* represents the conjugate transpose,
X is a vector containing a known pilot or known reference symbol (RS) sequence;
・

Is the variance of the channel noise,
・

Is the least squares (LS) estimate of the channel, where y is the vector of received RS symbols.

  The MMSE estimator provides much better performance than the LS channel estimator alone, especially under low SNR scenarios. However, a major drawback of the MMSE estimator is its high computational complexity due to increased consumption of system resources and increased system overhead from evaluating the entire MMSE expression.

  In the MMSE equation, X represents a known reference signal (RS) sequence transmitted by the UE. In LTE standard systems, sequence hopping and group hopping can accommodate uplink RS sequences, and when sequence / group hopping is enabled, the above MMSE expression is per slot (0.5 ms) Note that two matrix inversions need to be performed. For one user with one RB assignment, the matrix size is 12 × 12, while for all 48 RB assignments for one user, the matrix size is 576 × 576. Such real-time matrix inversion is computationally intensive in actual implementation. The second point to note is that this MMSE equation is found to be inaccurate because the user equipment (or mobile node) moves at a higher speed.

Some reduction in the complexity of the MMSE technique described above can be realized by the proposed method as follows. (1) Replace the term (XX ^* ) ⁻¹ with its expected value E {(XX ^* ) ⁻¹ }, (2) the signal constellation is the same on all subcarriers in the RS symbol, and all constellations E {(XX ^* ) ^-1 } = E {| 1 / Xk | ² } I, where I is the identity matrix, and (3) average SNR = E {| Define Xk | ² } / σ _N ² , term β = E {| Xk | ² } / E {| 1 / Xk | ² }, and term σ _N ² (XX ^* ) ⁻¹ = (β / SNR) I Where β is a constant that depends only on the signal constellation, (4) β = 1 for the QPSK constellation, β = 0.5294 for the 16QAM constellation, Β = 0.3724 for QAM transmission, 5) In the LTE _{uplink, R HH} matrix is sub-carrier spacing and channels r. m. Only depends on s delay spread.

式（２）におけるような実施形態の利点は、計算されるべき反転が１つしかなく、行列が定数値を含むことである。別のＳＮＲ推定値が受信されるまで（たとえ、シーケンス・ホッピングおよびグループ・ホッピングがイネーブルにされても）、同じ行列がいくつかのサブフレームのために使用されることが可能である。 From these assumptions, the MMSE channel estimation equation can be modified as follows.

The advantage of the embodiment as in equation (2) is that there is only one inversion to be calculated and the matrix contains constant values. The same matrix can be used for several subframes until another SNR estimate is received (even if sequence hopping and group hopping are enabled).

ここで、γ_ｆおよびγ_ｌは周波数相関関数および時間相関関数を表し、ｋは副搬送波数を表し、ｌはシンボル時間指数を表す。 By taking advantage of the simplicity in generating the channel autocorrelation of the LTE uplink RS pattern, further complexity reduction can be achieved. The channel autocorrelation for the time-frequency OFDM grid may be written as the product of the frequency correlation function and the time correlation function.

Here, γ _f and γ _l represent a frequency correlation function and a time correlation function, k represents the number of subcarriers, and l represents a symbol time index.

と書かれることが可能であり、ここで、１／Ｔは、（ＬＴＥでは１５ＫＨｚである）副搬送波分離であり、τ_ｒｍｓは、チャネルのｒ．ｍ．ｓ遅延スプレッドである。 By assuming an exponential decay power delay profile (which is a widely accepted industry standard for channel power profiles), the frequency correlation function is

Where 1 / T is the subcarrier separation (which is 15 KHz in LTE) and τ _rms is the r. m. s delay spread.

By assuming a Jakes spectrum for Doppler spread, the time correlation function is
γ _t (l) = J _o (2πf _max lT _s ) (5)
Can be written.

Here, J _o is the first-order and first-type Bessel functions, f _max is the maximum Doppler spread, and T _s is the OFDM symbol duration.

と書かれることが可能である。 In the LTE uplink RS pattern, the time correlation function between the RS patterns is 1. This is due to the fact that all reference symbols are in the same OFDM symbol. Therefore, the channel autocorrelation in equation (3) is

Can be written.

The procedure for low complexity MMSE channel estimation for LTE uplink receivers is as follows. The present invention generates R _HH as shown in equations (6) and (4) for all RB allocations (1, 2, 3, 4,... N _RB ) for channel estimation, Store in memory. R at the time of generation of R _HH. m. It is safe to assume a value of 2 microseconds for the s delay spread. The simulation shows that the accuracy of the channel estimate is r. m. It shows that it is not very sensitive to the s-delay spread value. This implies storage of the matrix size (12 × 12, 24 × 24,... 576 × 576). This is a single event and does not have to be repeated. If storage is a problem, the storage may be generated in real time, but this is not an efficient use of computing resources.

を推定し、ここで、ｙは受信されたＲＳシンボルのベクトルであり、Ｘは、ＵＬ ＲＳシンボルにおける既知の送信された一定振幅ゼロ自己相関（ＣＡＺＡＣ）シーケンスのベクトルである。ＲＳグループ・ホッピングおよびシーケンス・ホッピングがディセーブルにされた場合は、Ｘのコンテンツはそのユーザには同じままである。ＲＳグループ・ホッピングおよびシーケンス・ホッピングがイネーブルにされた場合は、Ｘのコンテンツはスロットまたはサブフレームごとに変わることができる。 For each slot (0.5 ms duration)

Where y is a vector of received RS symbols and X is a vector of known transmitted constant amplitude zero autocorrelation (CAZAC) sequences in UL RS symbols. If RS group hopping and sequence hopping are disabled, the contents of X remain the same for the user. If RS group hopping and sequence hopping are enabled, the contents of X can change from slot to slot or subframe.

を使用して取得される。 The present invention obtains the SNR estimate from the previous slot or subframe if the UE is continuously transmitting, or the SNR estimate is the channel estimate in the case of intermittent transmission.

Is obtained using.

を生成する。 Low using known values for β (1 for QPSK, 0.5294 for 16-QAM, 0.3724 for 64-QAM), SNR estimate, and R _HH Complex MMSE matrix

Is generated.

を使用してＲＳの正確なチャネル推定値を生成する。

The present invention provides a low complexity MMSE matrix and a pre-calculated LS channel estimate similar to the case of equation (2) repeated below.

Is used to generate an accurate channel estimate of the RS.

  From the RS MMSE estimate provided in the present invention, a data channel estimate can be generated using linear interpolation.

  Simulation results are shown in the following graphs 1-10, where (1) channel estimation and demodulation performance is QPSK at UE mobility of 3 km / hr, 60 km / hr, 120 km / hr, and 350 km / hr , 16QAM, 64QAM modulation, (2) the receiver comprises a single attenuator at the eNodeB, (3) the uncoded modulation symbol error rate SER vs. SNR is plotted, (4) 2 GHz carrier frequency is assumed to be 5 Hz, 100 Hz, 200 Hz, and 600 Hz, respectively, with maximum Doppler spreads at 3 km / hr, 60 km / hr, 120 km / hr, and 350 km / hr, and (5) two receivers. For BER vs. SNR by antenna, turbo code, and interleaving Coding rate and according to the channel) additional performance gain of about 10dB is expected, (6) RS channel estimates is applied directly to the data channel for demodulation in the simulation results.

Graphs 1-4 provide a QPSK demodulation performance comparison as follows.

Graphs 5-7 provide a 16QAM demodulation performance comparison as follows.

Graphs 8-10 provide a 64QAM demodulation performance comparison as follows.

  The low complexity MMSE channel estimation method of the present invention provides equivalent accuracy to support QPSK modulation up to 350 km / hour with reduced overhead, reduced number of calculations, and improved system performance.

  For 16QAM modulation, channel estimation accuracy can support mobility up to 120 km / hr. At higher mobility using 16QAM, inter-carrier interference (ICI) becomes dominant and techniques such as MMSE that consider noise as AWGN cannot improve channel estimation performance by increasing SNR. As discussed in the second embodiment, techniques for performing ICI cancellation combined with channel estimation may be more effective for such high mobility.

  With 64QAM modulation, channel estimation accuracy can support mobility up to 60 km / hr. With 64QAM modulation, the accuracy of channel estimation is not robust, so ICI at 120 km / hour cannot provide sufficient demodulation performance as shown in graph 10. Techniques that perform joint ICI cancellation and channel estimation may be more effective for such dense constellations at their higher mobility using 64QAM modulation.

  Note that accurate estimation of signal-to-noise ratio (SNR) is an essential processing step in an eNodeB receiver for use according to the present invention of low complexity least mean square error (RC-MMSE) channel estimation. . The performance of the technique of the present invention (low complexity MMSE channel estimation) has some sensitivity to the estimated SNR. In graph 11, the demodulated SER versus actual SNR for SNR estimation errors in the range of +/− 3 dB is plotted. Note that overestimation of the SNR will yield slightly better performance, while underestimation of the SNR will yield worse performance. In order to limit the performance degradation, it is desirable that the SNR be estimated within -3 dB of the actual channel SNR.

  Even though overestimation of the SNR can help RC-MMSE channel estimation performance, the overestimated SNR can be improved by scheduling (for resource allocation), modulation and coding control, handoff, power control, and soft decisions. There is a possibility of causing a performance degradation to the algorithm for decoding. It is therefore essential to estimate the SNR as accurately as possible in order to minimize overall system performance degradation.

  The more accurate SNR estimate that is part of the second aspect of the invention consists of the following two part estimates. (I) signal power estimation, and (ii) noise plus interference variance estimation. Signal power estimation is relatively simple and can be performed from channel estimates. However, accurate estimation of noise or interference variance is complex.

  A typical algorithm for noise variance estimation assumes that the noise statistic remains constant (white noise) over the entire allocated bandwidth. In an actual LTE deployment, with N = 1 frequency reuse, all cell sites in the network use the same set of physical resource blocks (PRBs) in all sectors. Even with interference mitigation through coordination, there is considerable overlap in PRB allocation from neighboring cells / sectors. Partially or completely overlapping resource blocks from distant cells cause co-channel interference (CCI).

  Further, adjacent channel interference (ACI) resulting from improper filtering of adjacent channels and inter-carrier interference (ICI) due to spectral broadening from Doppler spread at high mobility of user equipment (UE) Exists. These interferences are generally not white but colored in nature and the spectral density is not uniform across the entire bandwidth. In practice, the total noise present in the uplink receiver often consists of white Gaussian noise along with correlated colored noise.

  In the following section, methods and procedures for SNR estimation from an RC-MMSE channel estimator in an eNodeB receiver are proposed. The SNR estimated from the physical random access channel (PRACH) preamble or sounding reference signal (SRS) of the user equipment is used for physical uplink shared channel (PUSCH) first slot demodulation reference signal (DMRS) channel estimation. Can be done. The second slot DRS channel estimation may use the updated SNR estimated from the first slot DMRS using the following method.

As part of the signal interference to noise ratio estimation in the LTE uplink, the received RS signal after FFT in the nth symbol for the kth subcarrier is:
Y _{n, k} = S _{n, k} H _{n, k} + In _{, k} + W _{n, k}
Y _{n, k} = S _{n, k} H _{n, k} + Z _{n, k} (7)
Can be written,
here,
S _{n, k} is the transmitted RS symbol,
H _{n, k} is the value of the channel frequency response (CFR),
In _{, k} is colored noise (interference) including CCI, ACI, and ICI;
W _{n, k} is a white Gaussian noise sample,
Z _{n, k} is the total noise term.

It is assumed that degradation due to imperfect synchronization, transceiver nonlinearity, etc. is included in W _{n, k} . Furthermore, it is assumed that the CFR does not change within the observation time.

  The reference signal (RS) used for channel estimation in the LTE uplink occurs in the same SC-FDMA symbol. This is true for all LTE uplink physical channels (SRS, PRACH, PUCCH, and PUSCH). Since the proposed SINR estimation method uses the RS channel estimation value and the knowledge of the code symbol in the RS, the subscript n representing the symbol number is omitted in the equation.

を提供し、推定値の平均二乗誤差（ＭＳＥ）は、グラフ１２に示されているように−３０ｄＢほどの低い推定値である。したがって、チャネル推定値は、

のような平均受信信号電力を生成するために使用されることが可能である。

The signal power estimation of the proposed method is simple. This estimation uses channel estimates to obtain useful signal power levels. Low complexity least mean square error (RC-MMSE) channel estimation [1] is the channel estimate,

And the mean square error (MSE) of the estimate is an estimate as low as −30 dB as shown in graph 12. Therefore, the channel estimate is

Can be used to generate an average received signal power such as

雑音プラス干渉サンプル、

を利用することは、

と書かれることが可能である。 The present invention proposes the use of a signal recovery (SR) estimator to estimate the level of interference plus noise. This approach for noise power estimation is based on finding the difference between the noisy received sample in the frequency domain and the best hypothesis of the no-noise received sample. The code symbol value S _k in RS is known in the eNodeB receiver. Therefore, the channel estimate,

Noise plus interference samples,

To use

Can be written.

に存在する雑音および干渉の様々な源、ならびにそれらの様々なスペクトル特性および相互相関のために複雑である。前述のように、受信された信号の干渉成分は、ＣＣＩ、ＡＣ
Ｉ、およびＩＣＩから成る。この論考のために、白色ガウス雑音はＷ_ｋ＝Ｎ（０，σ_０ ^２）
と、干渉項はＩ_ｋ＝Ｎ（０，σ_ｋ ^２）とモデル化され、ここでσ_ｋは局所標準偏差である。 An accurate estimate of interference plus noise variance is

Are complicated by the various sources of noise and interference present in them, and their various spectral properties and cross-correlations. As described above, the interference components of the received signal are CCI, AC
I and ICI. For this discussion, white Gaussian noise is W _k = N (0, σ ₀ ² ).
And the interference term is modeled as I _k = N (0, σ _k ² ), where σ _k is the local standard deviation.

The method of the present invention proposes noise variance estimation using signal recovery from channel estimates. Thus, there are also additional components for noise due to channel estimation errors. The channel estimation error can be modeled as a zero-mean complex white Gaussian process E _k = N (0, σ _g ² ) of variance.

は、

とモデル化されることが可能であり、ここで、

である。
成分、

は、チャネル推定誤差分散を含む白色ガウス雑音部分の分散を含み、

は、有色干渉雑音の分散を含む。
目的は、ＳＮＲを見つけるために使用されることが可能である

を推定することである。

である場合は、全雑音は白色であると仮定されることが可能であり、そうでない場合は、全雑音は有色であることに留意されたい。 Assuming that the interference term and the white noise term are not correlated, the total noise term

Is

And can be modeled here, where

It is.
component,

Contains the variance of the white Gaussian noise part, including the channel estimation error variance,

Includes the dispersion of colored interference noise.
Objective can be used to find SNR

Is to estimate.

Note that the total noise can be assumed to be white, otherwise the total noise is colored.

と仮定し、したがって、受信サンプルにおける総雑音はガウス分布関数に従う。 The methods and procedures described herein are:

Therefore, the total noise in the received samples follows a Gaussian distribution function.

The present invention uses the signal power obtained by equation (8) to generate an SNR estimate and calculates the noise variance of the noise sample defined by equation (9) as follows:

The performance of the SNR estimator in the present invention obtained from equation (10) is shown in graph 13.

  This graph shows the SNR estimation error as a function of the actual SNR. SNR estimation error = actual SNR−estimated SNR. As can be seen from graph 13, the SNR is overestimated at a lower value of the actual SNR (which means that the noise variance is underestimated), and the SNR is at a higher value of the actual SNR. Underestimated (this means that the noise variance is overestimated).

  Overestimation of noise variance at high values of SNR is due in part to channel estimation errors. At high values of SNR, the channel estimation error variance is equivalent to the measured noise / interference variance, and thus higher noise / interference is reported than it actually is.

  To compensate for the SNR estimation error, it is simple and straightforward to assume that there is a direct relationship between the level of interference plus noise and the channel estimation error. In order to take into account the effect of the channel estimation error variance on the total noise variance estimation and thus to compensate for the total noise overestimation, it is proposed to introduce a compensation factor ρ. The factor ρ is defined as the ratio between the variance of the channel estimation error and the variance of interference plus noise.

Using this ratio ρ, a more accurate estimate of the level of SNR can be obtained from equation (11).

  The ratio ρ in equation (11) compensates for the overestimation caused by the channel estimation error in equation (10). The value of ρ can be optimized between 0 and 1. The factor ρ depends mainly on the performance of the channel estimator. The performance of the channel estimator can be obtained from the mean square channel estimation error (MSE) simulation of the channel estimator. In the ideal case of full channel estimation, ρ is equal to zero.

The compensated SNR estimation error as a function of actual SNR is shown in graph 14 for various values of ρ.

In addition, SNR estimation errors for various values of the compensation factor ρ are provided in Table 1.

  As can be seen from Graph 4 and Table 1, SNR estimation error compensation by ρ results in an overall smoothing of the SNR estimation error value. There is still some overestimation at low SNR, but underestimation is minimized at high values of SNR. The SNR estimation error is widely in the range of −6 dB / + 4 dB with a compensation factor of ρ = 0.9. Note that as a benchmark, this estimated performance is better than the accuracy requirement of +/− 9 dB power measurement specified in the CDMA radio interface standard.

  In order to obtain an accurate RS channel estimate from equation (2), it is also necessary to utilize the frequency correlation between subcarriers in the RS symbol. At low user equipment mobility, these RS channel estimates can be applied directly for data channel equalization. This is due to the fact that there is a high correlation in the time domain between the RS channel estimate and the data channel estimate.

  However, at higher user equipment mobility (typically> 60 km / hour), the correlation in the time domain between the RS channel estimate and the data channel estimate decreases. Therefore, applying the RS channel estimate directly to equalize the data channel will slightly degrade SNR vs. SER performance. For higher user equipment mobility, the data channel estimate needs to be interpolated from the RS channel estimate. There are several techniques for interpolation, including linear, bilinear, and quadratic techniques. These approaches to interpolation are static and the same approach is used for all UE mobility and therefore does not provide optimal SNR vs. SER performance.

  An adaptive method of data channel interpolation from the RS channel is proposed in the present invention. In this approach, the time correlation function between the data channel and the RS channel is used for interpolation. The time correlation of the symbol-to-symbol channel in a given subframe is advantageously used for data channel interpolation from the RS channel estimate. Since the time correlation function depends on the Doppler spread, this approach first estimates the Doppler spread due to UE mobility.

Two approaches are proposed for Doppler estimation.
(I) RS channel estimate cross-correlation based technique (ii) RS channel estimate phase linearity based technique

  With knowledge of the estimated Doppler spread, a time correlation between the data channel and the RS channel can be obtained and used for interpolation of the data channel estimate from the RS channel estimate. is there.

  Referring to FIG. 2, which shows uplink PRB and RS structure, this data interpolation method can be implemented for each data subcarrier over 14 symbols of a subframe. Since all these 14 symbols are at the same frequency for a given subcarrier, the frequency correlation is single across all 14 symbols for a given subcarrier. However, the time correlation across these symbols will vary depending on the symbol interval and UE mobility. By estimating the Doppler spread and using it to generate the temporal correlation between the subcarriers over these symbols, it is possible to accurately interpolate the data channel estimate from the RS channel estimate.

  This third aspect of the invention has three main steps. (1) generating a user equipment Doppler spread estimate from the RS channel estimate; (2) generating a time correlation matrix of data symbols for RS symbols using the Doppler estimate from step 1; ) Interpolating the data channel estimate from the RS channel estimate using the time correlation matrix from step 2.

In step 1, a user equipment Doppler spread estimate is generated using the cross correlation between the two RS symbols in slot 0 and slot 1 of the uplink subframe.
From equation (5)
γ _t (l) = J ₀ (2πf _max lT _s )
Is obtained,
here,
J ₀ is the zero order and first kind Bessel functions,
f _max is the maximum Doppler spread of the UE,
T _s is the OFDMA / SCFDMA total symbol duration (symbol plus CP duration);
l is the symbol index.
In graph 15, the time correlation between RSs in slot 0 and slot 1 is plotted as a function of maximum Doppler spread.

ここで、ＲＳ_ｓ０およびＲＳ_ｓ１は、スロット０およびスロット１基準信号であり、Ｎ_{ＲＢ＿ｌｅｎ}は、ＲＢの割当回数１２であり、^＊は、複素共役を示す。 As part of step 1 of the method in this aspect of the invention, the Doppler estimate is derived from slot 0 and slot 1 obtained by the RC-MMSE technique, which is used to generate a cross correlation C _{s0 — s1} as follows: Generated using RS channel estimates.

Here, RS _s0 and RS _s1 are slot 0 and slot 1 reference signals, N _{RB_len} is the number of RB allocations 12, and ^* indicates a complex conjugate.

The value obtained from step 1 is calculated as in equation (5) and stored in the DSP memory with a finite number of f _max values (50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600 Hz). The f _max corresponding to the minimum error from step (2) is considered the best approximation for the Doppler spread estimate for the UE.

This third approach in the present invention is based on a piecewise linear approximation to the phase of the RS channel estimate. As shown in graph 16 below, the estimated RS channel phase has a piecewise linear relationship with the RS channel estimate samples.

If φ is defined as the phase of the RS channel estimate, φw. r. The derivative of t time provides an estimate of the maximum Doppler spread. This differentiation can be performed equally by taking the slope of the RS channel estimate phase versus sample relationship shown in graph 16.

The procedure for obtaining a Doppler spread can be summarized as follows. First, RS channel estimation is performed using the RC-MMSE method of the present invention described above, and second, the phase of the lowest subcarrier channel estimate and the highest subcarrier channel estimate is calculated (this Can be calculated by tan ⁻¹ (Y / X), where Y is the imaginary part of the channel estimate and X is the real part of the channel estimate), third, The difference between the phases divided by the equivalent sample time is taken to obtain 2πf _max .

である。 The estimated Doppler spread is used to generate the time correlation required for data channel interpolation. Two time correlation matrices need to be generated. One is the pilot autocorrelation matrix, R _pp , and the other is the cross-correlation between the noisy pilot channel estimate and the data channel estimate,

It is.

２ＧＨｚにおける３ｋｍ／時のＵＥ移動度

２ＧＨｚにおける３０ｋｍ／時のＵＥ移動度

２ＧＨｚにおける６０ｋｍ／時のＵＥ移動度

２ＧＨｚにおける１２０ｋｍ／時のＵＥ移動度

２ＧＨｚにおける３６０ｋｍ／時のＵＥ移動度 In the LTE uplink resource block shown in FIG. 2, the time autocorrelation between two pilot channel estimates in a row, R _pp is a 2 × 2 matrix and is generated from equation (5) It is possible. Matrix values for UE mobility at 3 km / hr, 30 km / hr, 120 km / hr, and 360 km / hr are shown below.

UE mobility at 3km / hr at 2GHz

UE mobility at 30 km / h at 2 GHz

UE mobility at 2 km at 60 km / h

UE mobility at 120 km / h at 2 GHz

UE mobility at 360 km / h at 2 GHz

が、本発明のＲＣ−ＭＭＳＥ計算を使用してＬＴＥアップリンクＲＳパターンから生成されることが可能である。これは、１４×２行列であり、様々なＵＥ移動度条件に関して生成されることが可能である。３ｋｍ／時、３０ｋｍ／時、１２０ｋｍ／時、および３６０ｋｍ／時のＵＥ移動度に関する行列値が以下に示されている。
１．００００ ０．９９９９
１．００００ ０．９９９９
１．００００ ０．９９９９
１．００００ ０．９９９９
１．００００ １．００００
１．００００ １．００００
１．００００ １．００００
１．００００ １．００００
１．００００ １．００００
１．００００ １．００００
０．９９９９ １．００００
０．９９９９ １．００００
０．９９９９ １．００００
０．９９９９ １．００００
（ａ）３ｋｍ／時のＵＥ移動度に関する

行列
０．９９８９ ０．９８７５
０．９９９５ ０．９８９８
０．９９９９ ０．９９２０
１．００００ ０．９９３９
０．９９９９ ０．９９５５
０．９９９５ ０．９９６９
０．９９８９ ０．９９８０
０．９９８０ ０．９９８９
０．９９６９ ０．９９９５
０．９９５５ ０．９９９９
０．９９３９ １．００００
０．９９２０ ０．９９９９
０．９８９８ ０．９９９５
０．９８７５ ０．９９８
（ｂ）３０ｋｍ／時のＵＥ移動度に関する

行列
０．９９５５ ０．９５０４
０．９９８０ ０．９５９７
０．９９９５ ０．９６８１
１．００００ ０．９７５５
０．９９９５ ０．９８２０
０．９９８０ ０．９８７５
０．９９５５ ０．９９２０
０．９９２０ ０．９９５５
０．９８７５ ０．９９８０
０．９８２０ ０．９９９５
０．９７５５ １．００００
０．９６８１ ０．９９９５
０．９５９７ ０．９９８０
０．９５０４ ０．９９５５
（ｃ）６０ｋｍ／時のＵＥ移動度に関する

行列
０．９８２０ ０．８０８８
０．９９２０ ０．８４３６
０．９９８０ ０．８７５４
１．００００ ０．９０３９
０．９９８０ ０．９２８９
０．９９２０ ０．９５０４
０．９８２０ ０．９６８１
０．９６８１ ０．９８２０
０．９５０４ ０．９９２０
０．９２８９ ０．９９８０
０．９０３９ １．００００
０．８７５４ ０．９９８０
０．８４３６ ０．９９２０
０．８０８８ ０．９８２０
（ｄ）１２０ｋｍ／時のＵＥ移動度に関する

行列
０．８４３６ −０．１３８３
０．９２８９ −０．００８６
０．９８２０ ０．１３６９
１．００００ ０．２９１５
０．９８２０ ０．４４７２
０．９２８９ ０．５９６２
０．８４３６ ０．７３０７
０．７３０７ ０．８４３６
０．５９６２ ０．９２８９
０．４４７２ ０．９８２０
０．２９１５ １．００００
０．１３６９ ０．９８２０
−０．００８６ ０．９２８９
−０．１３８３ ０．８４３６
（ｅ）３６０ｋｍ／時のＵＥ移動度に関する

行列 Similarly, a cross-correlation matrix for noisy pilot channel estimates and data channel estimates,

Can be generated from the LTE uplink RS pattern using the RC-MMSE calculation of the present invention. This is a 14 × 2 matrix and can be generated for various UE mobility conditions. Matrix values for UE mobility at 3 km / hour, 30 km / hour, 120 km / hour, and 360 km / hour are shown below.
1.0000 0.9999
1.0000 0.9999
1.0000 0.9999
1.0000 0.9999
1.0000 1.0000
1.0000 1.0000
1.0000 1.0000
1.0000 1.0000
1.0000 1.0000
1.0000 1.0000
0.9999 1.0000
0.9999 1.0000
0.9999 1.0000
0.9999 1.0000
(A) Regarding UE mobility at 3 km / hour

Matrix 0.9989 0.9875
0.9995 0.9898
0.9999 0.9920
1.0000 0.9939
0.9999 0.9955
0.9995 0.9969
0.9989 0.9980
0.9980 0.9989
0.9969 0.9995
0.9955 0.9999
0.9939 1.0000
0.9920 0.9999
0.9898 0.9995
0.9875 0.998
(B) Regarding UE mobility at 30 km / hour

Matrix 0.9955 0.9504
0.9980 0.9597
0.9995 0.9681
1.0000 0.9755
0.9995 0.9820
0.9980 0.9875
0.9955 0.9920
0.9920 0.9955
0.9875 0.9980
0.9820 0.9995
0.9755 1.0000
0.9681 0.9995
0.9597 0.9980
0.9504 0.9955
(C) Regarding UE mobility at 60 km / hour

Matrix 0.9820 0.8088
0.9920 0.8436
0.9980 0.8754
1.0000 0.9039
0.9980 0.9289
0.9920 0.9504
0.9820 0.9681
0.9681 0.9820
0.9504 0.9920
0.9289 0.9980
0.9039 1.0000
0.8754 0.9980
0.8436 0.9920
0.8088 0.9820
(D) Regarding UE mobility at 120 km / h

Matrix 0.8436 -0.1383
0.9289 -0.0086
0.9820 0.1369
1.0000 0.2915
0.9820 0.4472
0.9289 0.5962
0.8436 0.7307
0.7307 0.8436
0.5962 0.9289
0.4472 0.9820
0.2915 1.0000
0.1369 0.9820
-0.0086 0.9289
-0.1383 0.8436
(E) Regarding UE mobility at 360 km / hour

matrix

と書かれることが可能であり、
ここで、

は、雑音の多いＲＳチャネル推定値と、サブフレームにおける全シンボルにわたるデータ・チャネル推定値との間の交差相関行列である。
Ｘは、パイロットまたは基準シンボル（ＲＳ）値を含むベクトルであり、

は、ＲＳトーンごとの雑音の分散であり、

は、ＲＣ−ＭＭＳＥチャネル推定手順からの

であるＲＳチャネル推定値である。 Based on MMSE estimation theory, the time domain interpolated data channel estimate from the RS channel estimate is

Can be written,
here,

Is the cross-correlation matrix between the noisy RS channel estimate and the data channel estimate across all symbols in the subframe.
X is a vector containing pilot or reference symbol (RS) values;

Is the variance of the noise per RS tone,

From the RC-MMSE channel estimation procedure

Is the RS channel estimate.

と書かれることが可能であり、ここで、βは、ＲＳコンステレーション（ＱＰＳＫのためには１、１６−ＱＡＭのためには０．５２９４、６４−ＱＡＭのためには０．３７２４）に依存し、ＳＮＲは、ＲＳトーンごとの信号対雑音比である。 By using a similar reduction used in obtaining the RC-MMSE channel estimate, equation (14) becomes

Where β depends on the RS constellation (1 for QPSK, 0.5294 for 16-QAM, 0.3724 for 64-QAM) SNR is the signal-to-noise ratio for each RS tone.

The procedure for data channel interpolation in the present invention from RS channel estimates includes the following steps. First, generate the RS channel estimates for slot 0 and slot 1 using the RC-MMSE procedure described through the use of equation (2) reproduced below. This generates the user's RS channel estimate for the user's assigned PRB (12 ^* PRB _{alloc_size} × 1 vector).

Second, the present invention estimates the user equipment Doppler spread, f _max , by using the method described above.

を使用する。 Third, the pre-computed matrix R _pp stored in memory corresponding to the estimated Doppler spread, f _max and

Is used.

  Fourth, with knowledge of the RS RC-MMSE estimate, SNR estimate, and RS constellation, the data channel estimate can be obtained using linear interpolation as in equation (15). .

The linear interpolation described above produces a vector 14 × 1 containing 12 data channel estimates. The interpolation is repeated 12 ^* PRB _{allc_size} times to get all data channel estimates

Interpolation involves multiplication of three matrices, (14 × 2) ^* (2 × 2) ^* (2 × 1). This interpolation provides a 14 × 1 vector containing 12 data channel estimates (and 2 RS estimates) for one subcarrier across all symbols. This corresponds to 84 complex multiplications for each subcarrier.

  In a 10 MHz system, this multiplication needs to be performed for 48 × 12 = 576 subcarriers every millisecond. Therefore, the number of complex multiplications is equal to 48.384 e06 times per second. This is relatively small compared to the 0.6900992e + 009 complex multiplications required for RC-MMSE RS channel estimation.

  It is impractical to store all time correlation values in memory corresponding to all possible Doppler spreads. As can be seen from FIG. 2, the correlation between RS symbols in a subframe varies from 1.0 to 0.1 over a range of 0-700 Hz Doppler. At a carrier frequency of 2.0 GHz, the UE speed of 360 km / hour changes to a 600 Hz Doppler spread. It is recommended to store correlation values corresponding to the following set of quantized Doppler spread values. [50 100 150 200 250 300 300 350 400 450 500 550 600] in Hz.

行列も、前述の同じセットのドップラー・スプレッドのためのメモリに記憶される。いかなる記憶要求をも回避するために、データ・チャネル補間器が必要とされるか否かに関するバイナリ判断をすることも可能である。時間相関値が０．８より低いか、またはドップラー・スプレッド値が３００Ｈｚより高い場合は、補間器が使用され、そうでない場合は、データ・チャネル推定値のための補間器は使用されない。 The performance degradation due to such a quantized approximation of Doppler spread is negligible.
_Rpp matrix, and

The matrix is also stored in memory for the same set of Doppler spreads described above. It is also possible to make a binary decision as to whether a data channel interpolator is needed to avoid any storage requirements. If the time correlation value is lower than 0.8 or the Doppler spread value is higher than 300 Hz, an interpolator is used, otherwise no interpolator for the data channel estimate is used.

  Simulation results under the following assumptions are shown in graphs 17 to 19 below. (1) Channel estimation and data demodulation performance with and without interpolation for QPSK / 16-QAM / 64-QAM at higher mobility is utilized; (2) The receiver has a single antenna at the eNodeB. Then, (3) the uncoded modulation symbol error rate SER vs. SNR is plotted, and (4) a carrier frequency of 2 GHz is assumed to be 600 Hz at a maximum Doppler spread of 360 km / hour. Note that with the two receiver antennas, turbo code, and interleaving, an additional approximately 10 dB performance gain is expected for BER vs. SNR (depending on code rate and channel).

Graphs 17-19 provide demodulation performance comparisons as follows.

  The foregoing embodiments of the present application are intended to be examples only. Those skilled in the art can make changes, modifications, and variations to the specific embodiments without departing from the scope of the present application. In the foregoing description, numerous details are set forth to provide an understanding of the present invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these details. Although the present invention has been disclosed with respect to a limited number of embodiments, those skilled in the art will appreciate numerous modifications and variations therefrom. The appended claims are intended to cover such modifications and variations as fall within the true spirit and scope of this invention.

Claims (40)

A method for estimating an uplink receiver channel in a wireless communication system having a transmitter and a receiver , comprising:
The method includes providing a transmitter and the uplink receiver on the radio communication system,
In order for the uplink receiver channel to estimate the channel for the uplink receiver,

Calculated using the low complexity minimum mean square error (MMSE) formula of
The low complexity least mean square error formula makes multiple assumptions about system performance and replaces the values in the minimum mean square error formula to reduce the complexity of formula computation, resulting in reduced processing time and system overhead usage will be reduced, methods. The low complexity minimum mean square error values in formula ^{(XX *) -1} is, conversion Erareru placed at its expected value ^{E {(XX *) -1}} , A method according to claim 1. One assumption regarding system performance is that the signal constellation is the same on all subcarriers in the reference signal (RS) symbol, with equal probability on all constellation points, and E {(XX ^* ) ⁻ The method of claim 1, wherein ¹ } = E {| 1 / Xk | ² } I, where I is an identity matrix. One assumption regarding system performance is that the average SNR = E {| Xk | ² } / σ _N ² and the value β = E {| Xk | ² } / E {| 1 / Xk | ² }, the value σ _N ² ( XX ^* ) ^-1 = ([beta] / SNR) I, where [beta] is a constant that depends only on the signal constellation.   One assumption regarding system performance is the following: β = 1 for QPSK constellation, β = 0.5294 for 16QAM constellation, β = 0.64 for 64-QAM transmission. The method of claim 1, wherein the method is to define one of 3724. One assumption regarding system performance is that the R _HH matrix is subcarrier spacing and the channel r. m. The method of claim 1, wherein it is assumed that it depends only on the s delay spread.   One assumption regarding system performance is that even if sequence hopping and group hopping are enabled, the same matrix is used for several subframes until another SNR estimate is received. The method of claim 1, which is assumed to be possible. The method of claim 1, wherein one replaced value in the low complexity least mean square error equation is obtained by taking advantage of simplicity in generating channel autocorrelation of one LTE uplink RS pattern. . The method of claim 1, wherein one assumption regarding system performance is to assume an exponentially decaying power delay profile. One replaced value sets the time correlation function between RS patterns to 1 for the LTE uplink RS pattern, which correlates with the fact that all reference symbols are in the same OFDM symbol. Item 2. The method according to Item 1. One assumption regarding system performance is that the accuracy of the channel estimate is r. m. Since the show is simulation that it is not very sensitive to s delay spread value, the r at the time of generation of R _HH. m. The method of claim 1, wherein a value of 2 microseconds is assumed for the s delay spread. One assumption regarding system performance is that every slot (0.5 ms duration)

The method of claim 1, wherein y is a received vector of RS symbols and X is a vector of known transmitted CAZAC sequences in UL RS symbols.   The method of claim 1, wherein one assumption regarding system performance is to obtain an SNR estimate from a previous slot / subframe if the user equipment is transmitting continuously. One assumption regarding system performance is that the simulation shows that the channel estimation performance is robust to SNR estimation errors, so for intermittent transmissions, the channel estimate

The method of claim 1, wherein the method is to obtain an SNR estimate using. One assumption regarding system performance is that the known values of β (1 for QPSK, 0.5294 for 16-QAM, 0.3724 for 64-QAM), SNR estimates, and R The method of claim 1, wherein the method is to use _HH .   The method of claim 1, further comprising generating a data channel estimate using linear interpolation and a minimum mean square error estimate of the reference signal.   The method of claim 1, wherein the SNR is estimated within −3 dB of the actual channel SNR.   The method of claim 1, further comprising interpolating the data channel using an adaptation step, an estimate of maximum Doppler spread, and a reference signal channel. A transmission system for estimating an uplink receiver channel in a wireless communication system, comprising :
The system comprises a transmitter and an uplink receiver on the wireless communication system ,
The uplink receiver channel, to estimate the channel for the uplink receiver,

Calculated using the low complexity minimum mean square error (MMSE) formula of
The low complexity minimum mean square error equation makes multiple assumptions regarding system performance and replaces the values in the minimum mean square error equation to reduce formula computation complexity, resulting in reduced processing time and system so that the overhead usage is reduced, the system. The low complexity minimum mean square error values in formula ^{(XX *) -1} is, conversion Erareru placed at its expected value ^{E {(XX *) -1}} , The system of claim 19. One assumption regarding system performance is that the signal constellation is the same on all subcarriers in the reference signal (RS) symbol, with equal probability on all constellation points, and E {(XX ^* ) ⁻ 21. The system of claim 19, wherein ¹ } = E {| 1 / Xk | ² } I, where I is an identity matrix. One assumption regarding system performance is that the average SNR = E {| Xk | ² } / σ _N ² and the value β = E {| Xk | ² } / E {| Xk | ² }, the value σ _N ² (XX ^* ) ^21. The system of claim 19, wherein ⁻¹ = (β / SNR) I is defined, where β is a constant that depends only on the signal constellation.   One assumption regarding system performance is the following: β = 1 for QPSK constellation, β = 0.5294 for 16QAM constellation, and β = 0.3724 for 64-QAM transmission. The system of claim 19, wherein the system is to define one of them. One assumption regarding system performance is that the R _HH matrix is the subcarrier spacing, and the r.H of the channel for LTE uplink transmission. m. 20. The system of claim 19, wherein the system is assumed to depend only on s delay spread.   One assumption regarding system performance is that even if sequence hopping and group hopping are enabled, the same matrix is used for several subframes until another SNR estimate is received. The system of claim 19, which is assumed to be possible. The system of claim 19, wherein one replaced value in the low complexity least mean square error equation is obtained by taking advantage of simplicity in generating channel autocorrelation of an LTE uplink RS pattern. The system of claim 19, wherein one assumption regarding system performance is to assume an exponential decay power delay profile. One replaced value sets the time correlation function between RS patterns to 1 for the LTE uplink RS pattern, which correlates with the fact that all reference symbols are in the same OFDM symbol. Item 20. The system according to Item 19. One assumption regarding system performance is that the accuracy of the channel estimate is r. m. Since the show is simulation that it is not very sensitive to s delay spread value, the r at the time of generation of R _HH. m. The system of claim 19, wherein a value of 2 microseconds is assumed for the s delay spread. One assumption regarding system performance is that every slot (0.5 ms duration)

20. The system of claim 19, wherein y is a received vector of RS symbols and X is a vector of known transmitted CAZAC sequences in UL RS symbols.   20. The system of claim 19, wherein one assumption regarding system performance is to obtain an SNR estimate from a previous slot / subframe if the user equipment is transmitting continuously. One assumption regarding system performance is that the simulation shows that the channel estimation performance is robust to SNR estimation errors, so for intermittent transmissions, the channel estimate

The system of claim 19, wherein the SNR estimate is obtained using. One assumption regarding system performance is that the known values of β (1 for QPSK, 0.5294 for 16-QAM, 0.3724 for 64-QAM), SNR estimates, and R The system of claim 19 , wherein the system is to use _HH .   20. The system of claim 19, wherein the transmitter generates a data channel estimate using linear interpolation and a minimum mean square error estimate of a reference signal.   The system of claim 19, wherein the SNR is estimated within −3 dB of the actual channel SNR. The system of claim 19, wherein the uplink receiver interpolates a data channel using an adaptation step, an estimate of maximum Doppler spread, and a reference signal channel. A wireless communication system configured to estimate an uplink receiver channel , comprising :
The system comprises a transmitter and a receiver ,
In order for the uplink receiver channel to estimate a channel for the uplink receiver,

The system is calculated using a low complexity minimum mean square error (MMSE) equation. 38. The system of claim 37 , wherein the receiver generates a data channel estimate using linear interpolation and a minimum mean square error estimate of a reference signal. 38. The system method of claim 37 , wherein the SNR is estimated within -3 dB of the actual channel SNR. 38. The system of claim 37 , wherein the receiver interpolates a data channel using an adaptation step, an estimate of maximum Doppler spread, and a reference signal channel.

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